SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3751–3760 of 4891 papers

TitleStatusHype
Characteristic Neural Ordinary Differential Equations—0
SpeechMoE2: Mixture-of-Experts Model with Improved Routing—0
Cycle Consistent Probability Divergences Across Different Spaces—0
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length—0
Using Convolutional Neural Networks to Detect Compression Algorithms—0
All Birds with One Stone: Multi-task Learning for Inference with One Forward Pass—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold—0
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and ClusteringCode0
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified